
## Netflix Release Year


# Dependencies and Setup
import matplotlib.pyplot as plt
import pandas as pd
import scipy.stats as st
import numpy as np
from scipy.stats import linregress
import seaborn as sns
import plotly.express as px
import plotly.graph_objects as go


from api_keys import netflix_api_key


# make paths and read csv raw data
data_path = "imdb.csv"

data = pd.read_csv(data_path)


data.head()


#get needed columns
year_data_df = data[['title','year','rating','vote']]

year_data_df.head()


#drop and null values
year_data_df = year_data_df.dropna()


year_data_df.head(20)


# dropping ALL duplicate values
year_data_df.drop_duplicates(subset ="title", keep = 'first', inplace = True)


#check for earliest date
earliestyear = year_data_df['year'].min()
earliestyear

1914.0


#create the bins to separate the age group and label the bins
bins = [0, 1939.9, 1949.9, 1959.9, 1969.9, 1979.9, 1989.9, 1999.9, 2009.9, 2019.9, 3000]
bin_names = ["Before 1940", "1940-1950", "1950-1960", "1960-1970", "1970-1980", "1980-1990", "1990-2000", "2000-2010", "2010-2020", "Post 2020"]


#add column to dataframe that describes that shows bins
year_data_df["Year Group"] = pd.cut(year_data_df["year"], bins, labels=bin_names, include_lowest=True)
year_data_df.head(20)


#GRAPH FOR DISTRIBUTION OF YEARS
#group by year group
yeargroup_df = year_data_df.groupby(['Year Group'])

ygcount_df = pd.DataFrame(yeargroup_df['year'].count())

#create bar chart
yeargroup_bar = ygcount_df.plot(kind="bar", title="Distribution of Movies by Year", color="crimson", legend=False)

#label the chart
yeargroup_bar.set_xlabel("Years")
yeargroup_bar.set_ylabel("Number of Movie")
plt.tight_layout

plt.show

<function matplotlib.pyplot.show(close=None, block=None)>


#sort data by rating
topmovies_df = year_data_df.sort_values("rating", ascending=False)

topmovies_df.head()


#GRAPH FOR AVERAGE VOTES PER MOVIE BY YEAR
topyear_df = topmovies_df.groupby(['Year Group'])

tycount_df = pd.DataFrame(topyear_df['vote'].mean())

#create bar chart
top25_bar = tycount_df.plot(kind="bar", title="Average Votes per Movie by Year", color="crimson", legend=False)

#label the chart
top25_bar.set_xlabel("Years")
top25_bar.set_ylabel("Average Votes per Movie")
plt.tight_layout

plt.show

<function matplotlib.pyplot.show(close=None, block=None)>


top25_df = topmovies_df[:25]

top25_df


## Netflix Kind Values


import matplotlib.pyplot as plt
import pandas as pd
import scipy.stats as st
import numpy as np
from scipy.stats import linregress
import seaborn as sns
import plotly.express as px
import plotly.graph_objects as go


netflix_pd = pd.read_csv("imdb.csv")


# remove duplicate movies
netflix_pd.drop_duplicates(subset ="title", keep = "first", inplace = True)


# clean and drop NAN in reduced data set
netflix_reduced = netflix_pd.loc[:, ["kind", "rating", "vote"]]
netflix_clean = netflix_reduced.dropna(how="any")


# assign kind values and unique labels
kind_unique = netflix_clean["kind"].unique()
kind_values = netflix_clean["kind"].value_counts()


# graph all the kinds distribution
# define parameters of the graph
labels = ['movie', 'tv short', 'video movie', 'tv movie', 'tv series', 'episode', 'tv mini series', 'video game']
sizes = [5213, 10, 1191, 744, 583, 469, 255, 17]
seperate = (0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01)


# plot the pie chart
plt.figure(figsize=(10,10))
plt.pie(sizes, explode=seperate, autopct="%1.1f%%", labels=labels)
plt.legend(loc="upper right")
plt.axis("equal")
plt.title("Netflix Kind Distribution")
plt.show()


movie_rating = netflix_clean.loc[netflix_clean["kind"] == "movie", "rating"]
video_rating = netflix_clean.loc[netflix_clean["kind"] == "video movie", "rating"]
movie_rating_mean = movie_rating.mean()
video_rating_mean = video_rating.mean()


# define parameters
labels = ["Digital Movies", "Video Movies"]
ratings = [movie_rating_mean, video_rating_mean]


# graph bar chart
plt.bar(labels, ratings, color="red", alpha=0.5, align="center")
plt.title("Digital Movie vs Video Movie Ratings")
plt.xlabel("Movie Type")
plt.ylabel("IMDB Ratings")
plt.ylim(0,10)
plt.show()


## Netflix Genre Types


import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
import plotly.graph_objects as go


netflix_pd = pd.read_csv("Netflix_Data.csv")


data_path = "Netflix_Data.csv"
data = pd.read_csv(data_path)


df = pd.read_csv("Netflix_Data.csv")
df.head()


df.isnull().sum()

Title                        0
Genre                     1710
Tags                        67
Languages                 1935
Series or Movie              0
Hidden Gem Score          2101
Country Availability        19
Runtime                      1
Director                  4708
Writer                    4330
Actors                    1925
View Rating               7024
IMDb Score                2099
Rotten Tomatoes Score     9098
Metacritic Score         11144
Awards Received           9405
Awards Nominated For      7819
Boxoffice                11473
Release Date              2107
Netflix Release Date         0
Production House         10331
Netflix Link                 0
IMDb Link                 2303
Summary                      9
IMDb Votes                2101
Image                        0
Poster                    3638
TMDb Trailer              8286
Trailer Site              8286
dtype: int64


df = df.drop(columns = [ 'Metacritic Score', 'Boxoffice', 'Production House', 'Netflix Link', 'IMDb Link',
        'Poster', 'TMDb Trailer', 'Trailer Site'], axis = 1)


df['Release Date']= pd.to_datetime(df['Release Date'])
df['Netflix Release Date']= pd.to_datetime(df['Netflix Release Date'])


df['Released_Year'] = pd.DatetimeIndex(df['Release Date']).year
df['Released_Year_Net'] = pd.DatetimeIndex(df['Netflix Release Date']).year


colors = ['black',] * 2
colors[0] = 'crimson'

count = df['Series or Movie'].value_counts()

fig = go.Figure(data=[go.Bar(
    x = df["Series or Movie"],
    y = count,
    text = count,
    textposition='auto',
    marker_color=colors # marker color can be a single color value or an iterable
)])
fig.update_traces(texttemplate='%{text:.2s}', textposition='outside')
fig.update_layout(title_text= 'Movie or Tv Series ?',
                  uniformtext_minsize=8, uniformtext_mode='hide',
                  barmode='group', xaxis_tickangle=-45,
                  yaxis=dict(
                  title='Quantity',
                  titlefont_size=14),
                  xaxis=dict(
                  title='Category',
                  titlefont_size=14))


df_movie = df[df['Series or Movie']=='Movie']
df_movie.head(1)


df_series = df[df["Series or Movie"] == "Series"]
df_series.head(1)


df_series_gen = df_series.dropna(subset=['Genre'])


colors_10 = ['DarkRed', 'FireBrick','Red', 'Crimson', 'IndianRed', 'slategray', 'gray', 'dimgrey', 'DarkSlateGrey', 'black']
series_gen_list = df_series_gen.Genre.str.split(',') #split the list into names
s_gen_list = {} #create an empty list
for genres in series_gen_list: # for any names in series_gen_list
    for genre in genres: # for any genre in genres
        if (genre in s_gen_list): #if this genre is already present in the s_gen_list
            s_gen_list[genre]+=1 # increase his value
        else:  # else
            s_gen_list[genre]=1 # Create his index in the list
s_gen_df = pd.DataFrame(s_gen_list.values(),index = s_gen_list.keys(),
                        columns = {'Counts of Genres in Tv Series'}) #Create a s_gen_df
s_gen_df.sort_values(by = 'Counts of Genres in Tv Series',ascending = False,inplace = True) #Sort the dataframe in ascending order
top_10_s_gen = s_gen_df[0:10]


fig = go.Figure(data=[go.Bar(
    x = top_10_s_gen.index,
    y = top_10_s_gen['Counts of Genres in Tv Series'],
    text = top_10_s_gen['Counts of Genres in Tv Series'],
    textposition='auto',
    marker_color=colors_10 # marker color can be a single color value or an iterable
)])
fig.update_traces(texttemplate='%{text:.2s}', textposition='outside')
fig.update_layout(title_text= 'Most Popular in TV Genre',
                  uniformtext_minsize=8, uniformtext_mode='hide',
                  yaxis=dict(
                  title='Quantity',
                  titlefont_size=14),
                  xaxis=dict(
                  title='Genres',
                  titlefont_size=14))


df_movie_gen = df_movie.dropna(subset=['Genre'])


movie_gen_list = df_movie_gen.Genre.str.split(', ') #split the list into names
m_gen_list = {} #create an empty list
for genres in movie_gen_list: # for any genres in movie_gen_list
    for genre in genres: # for any genre in genres
        if (genre in m_gen_list): #if this name is already present in the m_gen_list
            m_gen_list[genre]+=1 # increase his value
        else:  # else
            m_gen_list[genre]=1 # Create his index in the list
m_gen_df = pd.DataFrame(m_gen_list.values(),index = m_gen_list.keys(),
                        columns = {'Counts of Genres in Movies'}) #Create a m_gen_df
m_gen_df.sort_values(by = 'Counts of Genres in Movies',ascending = False,inplace = True) #Sort the dataframe in ascending order
top_10_m_gen = m_gen_df[0:10]


fig = go.Figure(data=[go.Bar(
    x = top_10_m_gen.index,
    y = top_10_m_gen['Counts of Genres in Movies'],
    text = top_10_m_gen['Counts of Genres in Movies'],
    textposition='auto',
    marker_color=colors_10 # marker color can be a single color value or an iterable
)])
fig.update_traces(texttemplate='%{text:.2s}', textposition='outside')
fig.update_layout(title_text= 'Most Popular Movie Genre',
                  uniformtext_minsize=8, uniformtext_mode='hide',
                  yaxis=dict(
                  title='Quantity',
                  titlefont_size=14),
                  xaxis=dict(
                  title='Genres',
                  titlefont_size=14))


df_series_imdb = df_series.dropna(subset=['IMDb Score'])
df_series_imdb = df_series_imdb.sort_values(by = 'IMDb Score', ascending = False)
top_s_imdb_10_list =df_series_imdb[:10]


fig = go.Figure(data=[go.Bar(
    x = top_s_imdb_10_list['Title'],
    y = top_s_imdb_10_list['IMDb Score'],
    text = top_s_imdb_10_list['IMDb Score'],
    textposition='auto',
    marker_color=colors_10 # marker color can be a single color value or an iterable
)])
fig.update_traces(texttemplate='%{text:.2s}', textposition='outside')
fig.update_layout(title_text= 'Top Rated Tv Series Rated by IMDB Score?',
                  uniformtext_minsize=8, uniformtext_mode='hide',
                  yaxis=dict(
                  title='IMDb Score',
                  titlefont_size=14),
                  xaxis=dict(
                  title='Titles',
                  titlefont_size=14))


df_movie_imdb = df_movie.dropna(subset=['IMDb Score'])
df_movie_imdb = df_movie_imdb.sort_values(by = 'IMDb Score', ascending = False)
top_m_imdb_10_list = df_movie_imdb[:10]


fig = go.Figure(data=[go.Bar(
    x = top_m_imdb_10_list['Title'],
    y = top_m_imdb_10_list['IMDb Score'],
    text = top_m_imdb_10_list['IMDb Score'],
    textposition='auto',
    marker_color=colors_10 # marker color can be a single color value or an iterable
)])
fig.update_traces(texttemplate='%{text:.2s}', textposition='outside')
fig.update_layout(title_text= 'Top Rated Movies Rated by IMDB Rating',
                  uniformtext_minsize=8, uniformtext_mode='hide',
                  yaxis=dict(
                  title='IMDb Score',
                  titlefont_size=14),
                  xaxis=dict(
                  title='Titles',
                  titlefont_size=14))


##Country of Orgin


import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import KMeans, AffinityPropagation
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
import warnings
warnings.filterwarnings("ignore")
import plotly as py
import plotly.graph_objs as go
import os
py.offline.init_notebook_mode(connected = True)
#print(os.listdir("../input"))
import datetime as dt
import missingno as msno
plt.rcParams['figure.dpi'] = 140


df = pd.read_csv('netflix_titles.csv')
df.head(3)


# Missing data

for i in df.columns:
    null_rate = df[i].isna().sum() / len(df) * 100 
    if null_rate > 0 :
        print("{} null rate: {}%".format(i,round(null_rate,2)))

director null rate: 30.68%
cast null rate: 9.22%
country null rate: 6.51%
date_added null rate: 0.13%
rating null rate: 0.09%


# Replacments

df['country'] = df['country'].fillna(df['country'].mode()[0])


df['cast'].replace(np.nan, 'No Data',inplace  = True)
df['director'].replace(np.nan, 'No Data',inplace  = True)

# Drops

df.dropna(inplace=True)

# Drop Duplicates

df.drop_duplicates(inplace= True)


df.isnull().sum()

show_id         0
type            0
title           0
director        0
cast            0
country         0
date_added      0
release_year    0
rating          0
duration        0
listed_in       0
description     0
dtype: int64


df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 7770 entries, 0 to 7786
Data columns (total 12 columns):
 #   Column        Non-Null Count  Dtype 
---  ------        --------------  ----- 
 0   show_id       7770 non-null   object
 1   type          7770 non-null   object
 2   title         7770 non-null   object
 3   director      7770 non-null   object
 4   cast          7770 non-null   object
 5   country       7770 non-null   object
 6   date_added    7770 non-null   object
 7   release_year  7770 non-null   int64 
 8   rating        7770 non-null   object
 9   duration      7770 non-null   object
 10  listed_in     7770 non-null   object
 11  description   7770 non-null   object
dtypes: int64(1), object(11)
memory usage: 789.1+ KB


df["date_added"] = pd.to_datetime(df['date_added'])

df['month_added']=df['date_added'].dt.month
df['month_name_added']=df['date_added'].dt.month_name()
df['year_added'] = df['date_added'].dt.year

df.head(3)


# Helper column for various plots
df['count'] = 1

# Many productions have several countries listed - this will skew our results , we'll grab the first one mentioned

# Lets retrieve just the first country
df['first_country'] = df['country'].apply(lambda x: x.split(",")[0])
df['first_country'].head()

# Rating ages from this notebook: https://www.kaggle.com/andreshg/eda-beginner-to-expert-plotly (thank you!)

ratings_ages = {
    'TV-PG': 'Older Kids',
    'TV-MA': 'Adults',
    'TV-Y7-FV': 'Older Kids',
    'TV-Y7': 'Older Kids',
    'TV-14': 'Teens',
    'R': 'Adults',
    'TV-Y': 'Kids',
    'NR': 'Adults',
    'PG-13': 'Teens',
    'TV-G': 'Kids',
    'PG': 'Older Kids',
    'G': 'Kids',
    'UR': 'Adults',
    'NC-17': 'Adults'
}

df['target_ages'] = df['rating'].replace(ratings_ages)
df['target_ages'].unique()

# Genre

df['genre'] = df['listed_in'].apply(lambda x :  x.replace(' ,',',').replace(', ',',').split(',')) 

# Reducing name length

df['first_country'].replace('United States', 'USA', inplace=True)
df['first_country'].replace('United Kingdom', 'UK',inplace=True)
df['first_country'].replace('South Korea', 'S. Korea',inplace=True)


data = df.groupby('first_country')['count'].sum().sort_values(ascending=False)[:10]

# Plot

color_map = ['#f5f5f1' for _ in range(10)]
color_map[0] = color_map[1] = color_map[2] =  '#b20710' # color highlight

fig, ax = plt.subplots(1,1, figsize=(12, 6))
ax.bar(data.index, data, width=0.5, 
       edgecolor='darkgray',
       linewidth=0.6,color=color_map)

#annotations
for i in data.index:
    ax.annotate(f"{data[i]}", 
                   xy=(i, data[i] + 150), #i like to change this to roughly 5% of the highest cat
                   va = 'center', ha='center',fontweight='light', fontfamily='serif')



# Remove border from plot

for s in ['top', 'left', 'right']:
    ax.spines[s].set_visible(False)
    
# Tick labels

ax.set_xticklabels(data.index, fontfamily='serif', rotation=0)

# Title and sub-title

fig.text(0.09, 1, 'Top 10 countries on Netflix', fontsize=15, fontweight='bold', fontfamily='serif')
fig.text(0.09, 0.95, 'The three most frequent countries have been highlighted.', fontsize=12, fontweight='light', fontfamily='serif')

fig.text(1.1, 1.01, 'Insight', fontsize=15, fontweight='bold', fontfamily='serif')

fig.text(1.1, 0.67, '''
The most prolific producers of
content for Netflix are, primarily,
the USA, with India and the UK
a significant distance behind.

It makes sense that the USA produces 
the most content as, afterall, 
Netflix is a US company.
'''
         , fontsize=12, fontweight='light', fontfamily='serif')

ax.grid(axis='y', linestyle='-', alpha=0.4)   

grid_y_ticks = np.arange(0, 4000, 500) # y ticks, min, max, then step
ax.set_yticks(grid_y_ticks)
ax.set_axisbelow(True)

#Axis labels

#plt.xlabel("Country", fontsize=12, fontweight='light', fontfamily='serif',loc='left',y=-1.5)
#plt.ylabel("Count", fontsize=12, fontweight='light', fontfamily='serif')
 #plt.legend(loc='upper right')
    
# thicken the bottom line if you want to
plt.axhline(y = 0, color = 'black', linewidth = 1.3, alpha = .7)

ax.tick_params(axis='both', which='major', labelsize=12)


import matplotlib.lines as lines
l1 = lines.Line2D([1, 1], [0, 1], transform=fig.transFigure, figure=fig,color='black',lw=0.2)
fig.lines.extend([l1])

ax.tick_params(axis=u'both', which=u'both',length=0)

plt.show()


country_order = df['first_country'].value_counts()[:11].index
data_q2q3 = df[['type', 'first_country']].groupby('first_country')['type'].value_counts().unstack().loc[country_order]
data_q2q3['sum'] = data_q2q3.sum(axis=1)
data_q2q3_ratio = (data_q2q3.T / data_q2q3['sum']).T[['Movie', 'TV Show']].sort_values(by='Movie',ascending=False)[::-1]




###
fig, ax = plt.subplots(1,1,figsize=(15, 8),)

ax.barh(data_q2q3_ratio.index, data_q2q3_ratio['Movie'], 
        color='#b20710', alpha=0.8, label='Movie')
ax.barh(data_q2q3_ratio.index, data_q2q3_ratio['TV Show'], left=data_q2q3_ratio['Movie'], 
        color='#221f1f', alpha=0.8, label='TV Show')


ax.set_xlim(0, 1)
ax.set_xticks([])
ax.set_yticklabels(data_q2q3_ratio.index, fontfamily='serif', fontsize=11)

# male percentage
for i in data_q2q3_ratio.index:
    ax.annotate(f"{data_q2q3_ratio['Movie'][i]*100:.3}%", 
                   xy=(data_q2q3_ratio['Movie'][i]/2, i),
                   va = 'center', ha='center',fontsize=12, fontweight='light', fontfamily='serif',
                   color='white')

for i in data_q2q3_ratio.index:
    ax.annotate(f"{data_q2q3_ratio['TV Show'][i]*100:.3}%", 
                   xy=(data_q2q3_ratio['Movie'][i]+data_q2q3_ratio['TV Show'][i]/2, i),
                   va = 'center', ha='center',fontsize=12, fontweight='light', fontfamily='serif',
                   color='white')
    

fig.text(0.13, 0.93, 'Top 10 countries Movie & TV Show split', fontsize=15, fontweight='bold', fontfamily='serif')   
fig.text(0.131, 0.89, 'Percent Stacked Bar Chart', fontsize=12,fontfamily='serif')   

for s in ['top', 'left', 'right', 'bottom']:
    ax.spines[s].set_visible(False)
    
#ax.legend(loc='lower center', ncol=3, bbox_to_anchor=(0.5, -0.06))

fig.text(0.75,0.9,"Movie", fontweight="bold", fontfamily='serif', fontsize=15, color='#b20710')
fig.text(0.81,0.9,"|", fontweight="bold", fontfamily='serif', fontsize=15, color='black')
fig.text(0.82,0.9,"TV Show", fontweight="bold", fontfamily='serif', fontsize=15, color='#221f1f')


fig.text(1.1, 0.93, 'Insight', fontsize=15, fontweight='bold', fontfamily='serif')

fig.text(1.1, 0.44, '''
Interestingly, Netflix in India
is made up nearly entirely of Movies. 

Bollywood is big business, and perhaps
the main focus of this industry is Movies
and not TV Shows.

South Korean Netflix on the other hand is 
almost entirely TV Shows.

The underlying resons for the difference 
in content must be due to market research
conducted by Netflix.
'''
         , fontsize=12, fontweight='light', fontfamily='serif')



import matplotlib.lines as lines
l1 = lines.Line2D([1, 1], [0, 1], transform=fig.transFigure, figure=fig,color='black',lw=0.2)
fig.lines.extend([l1])




ax.tick_params(axis='both', which='major', labelsize=12)
ax.tick_params(axis=u'both', which=u'both',length=0)

plt.show()


##Language


netflix_pd = pd.read_csv("NetflixOriginals.csv")


data_path = "NetflixOriginals.csv"
data = pd.read_csv(data_path)


df = pd.read_csv("NetflixOriginals.csv")
df.head()


common_languages=netflix_pd['Language'].value_counts().reset_index(name='total')
language_list=common_languages[common_languages['total']>=3]['index']


common_languages[common_languages['total']>3].plot.bar(x='index', y='total',rot=90)

<AxesSubplot:xlabel='index'>


## Netflix Runtime


netflix_pd = pd.read_csv("IMDB-Movie-Data.csv")
netflix_pd.head()


netflix_pd.drop_duplicates(subset ="Title", keep = "first", inplace = True)


netflix_runtime = netflix_pd[["Title","Year","Rating","Runtime (Minutes)"]]


netflix_runtime = netflix_runtime.dropna(how="any")


bins = [0, 59.99, 74.99, 89.99, 104.99, 119.99, 134.99, 149.99, 300]
bin_names = ["Less than 60", "60-75", "75-90", "90-105", "105-120", "120-135", "135-150", "More than 150"]


netflix_pd["Runtime Group (Minutes)"] = pd.cut(netflix_runtime["Runtime (Minutes)"], bins, labels=bin_names, include_lowest=True)


# theres a better way to do this BUT...
# get the mean rating of each bin
less_hour_rating = netflix_pd.loc[netflix_pd["Runtime Group (Minutes)"] == "Less than 60", "Rating"]
hour_rating = netflix_pd.loc[netflix_pd["Runtime Group (Minutes)"] == "60-75", "Rating"]
hour_15_rating = netflix_pd.loc[netflix_pd["Runtime Group (Minutes)"] == "75-90", "Rating"]
hour_30_rating = netflix_pd.loc[netflix_pd["Runtime Group (Minutes)"] == "90-105", "Rating"]
hour_45_rating = netflix_pd.loc[netflix_pd["Runtime Group (Minutes)"] == "105-120", "Rating"]
two_hour_rating = netflix_pd.loc[netflix_pd["Runtime Group (Minutes)"] == "120-135", "Rating"]
two_hour_15_rating = netflix_pd.loc[netflix_pd["Runtime Group (Minutes)"] == "135-150", "Rating"]
over_hour_rating = netflix_pd.loc[netflix_pd["Runtime Group (Minutes)"] == "More than 150", "Rating"]


less_hour_rating_mean = less_hour_rating.mean()
hour_rating_mean = hour_rating.mean()
hour_15_rating_mean = hour_15_rating.mean()
hour_30_rating_mean = hour_30_rating.mean()
hour_45_rating_mean = hour_45_rating.mean()
two_hour_rating_mean = two_hour_rating.mean()
two_hour_15_rating_mean = two_hour_15_rating.mean()
over_hour_rating_mean = over_hour_rating.mean()


# define parameters
labels = ["Less than 60", "60-75", "75-90", "90-105", "105-120", "120-135", "135-150", "More than 150"]
ratings = [0, hour_rating_mean, hour_15_rating_mean, hour_30_rating_mean, hour_45_rating_mean, two_hour_rating_mean, two_hour_15_rating_mean, over_hour_rating_mean]


# graph bar chart
plt.bar(labels, ratings, color="red", alpha=0.5, align="center")
plt.title("Runtime Ratings")
plt.xlabel("Runtime Bins (Minutes)")
plt.ylabel("Ratings")
plt.ylim(0, 10)
plt.xticks(rotation=75)

([0, 1, 2, 3, 4, 5, 6, 7],
 [Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, '')])

	Unnamed: 0	title	year	kind	genre	rating	vote	country	language	runtime	cast	director	composer	writer	runtimes
0	0	Dinosaur Planet	2003.0	tv mini series	['Documentary', 'Animation', 'Family']	7.7	474.0	['United States']	['English']	['50']	['Christian Slater', 'Scott Sampson']	NaN	NaN	['Mike Carrol', 'Mike Carroll', 'Georgann Kane']	NaN
1	1	Character	2021.0	movie	['Crime', 'Horror', 'Thriller']	8.3	46.0	['Japan']	['Japanese']	['125']	['Masaki Suda', 'Fukase', 'Mitsuki Takahata', ...	['Akira Nagai']	['Youki Kojima']	['Takashi Nagasaki', 'Takashi Nagasaki', 'Anna...	NaN
2	2	Get Up and Dance!	1994.0	video movie	['Family']	8.1	18.0	['United States']	['English']	['54']	['Paula Abdul', 'Aurorah Allain', 'Bill Bohl',...	['Steve Purcell']	NaN	NaN	NaN
3	3	The Rise and Fall of El Chapo	2016.0	tv movie	['Documentary']	6.9	42.0	['United States']	NaN	['85']	NaN	NaN	NaN	NaN	NaN
4	4	Sick - IMDb	NaN	NaN	['Thriller']	NaN	NaN	['United States']	NaN	NaN	['Marc Menchaca', 'Gideon Adlon', 'Dylan Spray...	['John Hyams']	NaN	['Katelyn Crabb', 'Kevin Williamson']	NaN

	title	year	rating	vote
0	Dinosaur Planet	2003.0	7.7	474.0
1	Character	2021.0	8.3	46.0
2	Get Up and Dance!	1994.0	8.1	18.0
3	The Rise and Fall of El Chapo	2016.0	6.9	42.0
4	Sick - IMDb	NaN	NaN	NaN

	title	year	rating	vote
0	Dinosaur Planet	2003.0	7.7	474.0
1	Character	2021.0	8.3	46.0
2	Get Up and Dance!	1994.0	8.1	18.0
3	The Rise and Fall of El Chapo	2016.0	6.9	42.0
5	8 Man	1992.0	5.5	93.0
6	What the #$*! Do We (K)now!?	2004.0	5.3	13432.0
7	Class of Nuke 'Em High Part II: Subhumanoid Me...	1991.0	4.5	2177.0
8	The Fighter	2010.0	7.8	351199.0
11	Neil Diamond: Greatest Hits Live	1988.0	8.0	81.0
12	7 Seconds	2005.0	4.8	7153.0
13	By Dawn's Early Light	1990.0	7.1	2898.0
14	Seeta Aur Geeta	1972.0	6.8	1818.0
15	Strange Relations	2001.0	7.6	768.0
16	Sesame Street Presents: The Street We Live On	2004.0	7.2	67.0
17	Lilo and Stitch	2012.0	7.2	22.0
18	Boycott	2001.0	7.2	901.0
19	Meat Loaf: Bat Out of Hell	1999.0	7.7	286.0
20	Aqua Teen Hunger Force	2000.0	7.6	23785.0
21	FernGully 2: The Magical Rescue	1998.0	4.6	1484.0
22	Lady Chatterley	1993.0	6.9	1605.0

	title	year	rating	vote	Year Group
0	Dinosaur Planet	2003.0	7.7	474.0	2000-2010
1	Character	2021.0	8.3	46.0	Post 2020
2	Get Up and Dance!	1994.0	8.1	18.0	1990-2000
3	The Rise and Fall of El Chapo	2016.0	6.9	42.0	2010-2020
5	8 Man	1992.0	5.5	93.0	1990-2000
6	What the #$*! Do We (K)now!?	2004.0	5.3	13432.0	2000-2010
7	Class of Nuke 'Em High Part II: Subhumanoid Me...	1991.0	4.5	2177.0	1990-2000
8	The Fighter	2010.0	7.8	351199.0	2010-2020
11	Neil Diamond: Greatest Hits Live	1988.0	8.0	81.0	1980-1990
12	7 Seconds	2005.0	4.8	7153.0	2000-2010
13	By Dawn's Early Light	1990.0	7.1	2898.0	1990-2000
14	Seeta Aur Geeta	1972.0	6.8	1818.0	1970-1980
15	Strange Relations	2001.0	7.6	768.0	2000-2010
16	Sesame Street Presents: The Street We Live On	2004.0	7.2	67.0	2000-2010
17	Lilo and Stitch	2012.0	7.2	22.0	2010-2020
18	Boycott	2001.0	7.2	901.0	2000-2010
19	Meat Loaf: Bat Out of Hell	1999.0	7.7	286.0	1990-2000
20	Aqua Teen Hunger Force	2000.0	7.6	23785.0	2000-2010
21	FernGully 2: The Magical Rescue	1998.0	4.6	1484.0	1990-2000
22	Lady Chatterley	1993.0	6.9	1605.0	1990-2000

	title	year	rating	vote	Year Group
3848	Dragon Family	2004.0	9.6	8.0	2000-2010
2021	Mortal Kombat: At The Movies	2012.0	9.5	6.0	2010-2020
7655	Band of Brothers	2001.0	9.4	398551.0	2000-2010
2906	The Sopranos: Season 6 Invitation to the Set	2005.0	9.4	47.0	2000-2010
1185	Widespread Panic: The Earth Will Swallow You	2002.0	9.3	13.0	2000-2010

	Title	Genre	Tags	Languages	Series or Movie	Hidden Gem Score	Country Availability	Runtime	Director	Writer	...	Netflix Release Date	Production House	Netflix Link	IMDb Link	Summary	IMDb Votes	Image	Poster	TMDb Trailer	Trailer Site
0	Lets Fight Ghost	Crime, Drama, Fantasy, Horror, Romance	Comedy Programmes,Romantic TV Comedies,Horror ...	Swedish, Spanish	Series	4.3	Thailand	< 30 minutes	Tomas Alfredson	John Ajvide Lindqvist	...	2021-03-04	Canal+, Sandrew Metronome	https://www.netflix.com/watch/81415947	https://www.imdb.com/title/tt1139797	A med student with a supernatural gift tries t...	205926.0	https://occ-0-4708-64.1.nflxso.net/dnm/api/v6/...	https://m.media-amazon.com/images/M/MV5BOWM4NT...	NaN	NaN
1	HOW TO BUILD A GIRL	Comedy	Dramas,Comedies,Films Based on Books,British	English	Movie	7.0	Canada	1-2 hour	Coky Giedroyc	Caitlin Moran	...	2021-03-04	Film 4, Monumental Pictures, Lionsgate	https://www.netflix.com/watch/81041267	https://www.imdb.com/title/tt4193072	When nerdy Johanna moves to London, things get...	2838.0	https://occ-0-1081-999.1.nflxso.net/dnm/api/v6...	https://m.media-amazon.com/images/M/MV5BZGUyN2...	https://www.youtube.com/watch?v=eIbcxPy4okQ	YouTube
2	Centigrade	Drama, Thriller	Thrillers	English	Movie	6.4	Canada	1-2 hour	Brendan Walsh	Brendan Walsh, Daley Nixon	...	2021-03-04	NaN	https://www.netflix.com/watch/81305978	https://www.imdb.com/title/tt8945942	Trapped in a frozen car during a blizzard, a p...	1720.0	https://occ-0-1081-999.1.nflxso.net/dnm/api/v6...	https://m.media-amazon.com/images/M/MV5BODM2MD...	https://www.youtube.com/watch?v=0RvV7TNUlkQ	YouTube
3	ANNE+	Drama	TV Dramas,Romantic TV Dramas,Dutch TV Shows	Turkish	Series	7.7	Belgium,Netherlands	< 30 minutes	NaN	NaN	...	2021-03-04	NaN	https://www.netflix.com/watch/81336456	https://www.imdb.com/title/tt6132758	Upon moving into a new place, a 20-something r...	1147.0	https://occ-0-1489-1490.1.nflxso.net/dnm/api/v...	https://m.media-amazon.com/images/M/MV5BNWRkMz...	NaN	NaN
4	Moxie	Animation, Short, Drama	Social Issue Dramas,Teen Movies,Dramas,Comedie...	English	Movie	8.1	Lithuania,Poland,France,Iceland,Italy,Spain,Gr...	1-2 hour	Stephen Irwin	NaN	...	2021-03-04	NaN	https://www.netflix.com/watch/81078393	https://www.imdb.com/title/tt2023611	Inspired by her moms rebellious past and a con...	63.0	https://occ-0-4039-1500.1.nflxso.net/dnm/api/v...	https://m.media-amazon.com/images/M/MV5BODYyNW...	NaN	NaN

	show_id	type	title	director	cast	country	date_added	release_year	rating	duration	listed_in	description
0	s1	TV Show	3%	NaN	João Miguel, Bianca Comparato, Michel Gomes, R...	Brazil	August 14, 2020	2020	TV-MA	4 Seasons	International TV Shows, TV Dramas, TV Sci-Fi &...	In a future where the elite inhabit an island ...
1	s2	Movie	7:19	Jorge Michel Grau	Demián Bichir, Héctor Bonilla, Oscar Serrano, ...	Mexico	December 23, 2016	2016	TV-MA	93 min	Dramas, International Movies	After a devastating earthquake hits Mexico Cit...
2	s3	Movie	23:59	Gilbert Chan	Tedd Chan, Stella Chung, Henley Hii, Lawrence ...	Singapore	December 20, 2018	2011	R	78 min	Horror Movies, International Movies	When an army recruit is found dead, his fellow...

	show_id	type	title	director	cast	country	date_added	release_year	rating	duration	listed_in	description	month_added	month_name_added	year_added
0	s1	TV Show	3%	No Data	João Miguel, Bianca Comparato, Michel Gomes, R...	Brazil	2020-08-14	2020	TV-MA	4 Seasons	International TV Shows, TV Dramas, TV Sci-Fi &...	In a future where the elite inhabit an island ...	8	August	2020
1	s2	Movie	7:19	Jorge Michel Grau	Demián Bichir, Héctor Bonilla, Oscar Serrano, ...	Mexico	2016-12-23	2016	TV-MA	93 min	Dramas, International Movies	After a devastating earthquake hits Mexico Cit...	12	December	2016
2	s3	Movie	23:59	Gilbert Chan	Tedd Chan, Stella Chung, Henley Hii, Lawrence ...	Singapore	2018-12-20	2011	R	78 min	Horror Movies, International Movies	When an army recruit is found dead, his fellow...	12	December	2018

	Title	Genre	Premiere	Runtime	IMDB Score	Language
0	Enter the Anime	Documentary	5-Aug-19	58	2.5	English/Japanese
1	Dark Forces	Thriller	21-Aug-20	81	2.6	Spanish
2	The App	Science fiction/Drama	26-Dec-19	79	2.6	Italian
3	The Open House	Horror thriller	19-Jan-18	94	3.2	English
4	Kaali Khuhi	Mystery	30-Oct-20	90	3.4	Hindi

	Rank	Title	Genre	Description	Director	Actors	Year	Runtime (Minutes)	Rating	Votes	Revenue (Millions)	Metascore
0	1	Guardians of the Galaxy	Action,Adventure,Sci-Fi	A group of intergalactic criminals are forced ...	James Gunn	Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S...	2014	121	8.1	757074	333.13	76.0
1	2	Prometheus	Adventure,Mystery,Sci-Fi	Following clues to the origin of mankind, a te...	Ridley Scott	Noomi Rapace, Logan Marshall-Green, Michael Fa...	2012	124	7.0	485820	126.46	65.0
2	3	Split	Horror,Thriller	Three girls are kidnapped by a man with a diag...	M. Night Shyamalan	James McAvoy, Anya Taylor-Joy, Haley Lu Richar...	2016	117	7.3	157606	138.12	62.0
3	4	Sing	Animation,Comedy,Family	In a city of humanoid animals, a hustling thea...	Christophe Lourdelet	Matthew McConaughey,Reese Witherspoon, Seth Ma...	2016	108	7.2	60545	270.32	59.0
4	5	Suicide Squad	Action,Adventure,Fantasy	A secret government agency recruits some of th...	David Ayer	Will Smith, Jared Leto, Margot Robbie, Viola D...	2016	123	6.2	393727	325.02	40.0

	title	year	rating	vote	Year Group
3848	Dragon Family	2004.0	9.6	8.0	2000-2010
2021	Mortal Kombat: At The Movies	2012.0	9.5	6.0	2010-2020
7655	Band of Brothers	2001.0	9.4	398551.0	2000-2010
2906	The Sopranos: Season 6 Invitation to the Set	2005.0	9.4	47.0	2000-2010
1185	Widespread Panic: The Earth Will Swallow You	2002.0	9.3	13.0	2000-2010
698	Yanni: Live at the Acropolis	1994.0	9.3	410.0	1990-2000
652	The Shawshank Redemption	1994.0	9.3	2461873.0	1990-2000
2807	Queensrÿche: Operation Livecrime	1991.0	9.3	146.0	1990-2000
282	Blue Planet II	2017.0	9.3	36474.0	2010-2020
2600	Pride 25: Body Blow	2003.0	9.3	8.0	2000-2010
3360	311: Live in Concert, New Orleans - 3-11 Day 2004	2004.0	9.3	113.0	2000-2010
1224	Smallville Season 3 Promo	2003.0	9.2	58.0	2000-2010
9183	Depeche Mode: Devotional	1993.0	9.2	914.0	1990-2000
7475	The World at War	1973.0	9.2	23729.0	1970-1980
4662	Behind the Scenes: One Tree Hill Season 6	2009.0	9.2	43.0	2000-2010
1657	The Godfather	1972.0	9.2	1702698.0	1970-1980
7065	Roy Orbison: Black and White Night 30	2017.0	9.2	52.0	2010-2020
753	Dream Theater: Live at Budokan	2004.0	9.2	713.0	2000-2010
6047	The Lex Series Laptop	2019.0	9.2	8.0	2010-2020
5317	Baseball	1994.0	9.2	4028.0	1990-2000
7187	Nine Inch Nails Live: And All That Could Have ...	2002.0	9.2	1251.0	2000-2010
4812	Game of Thrones	2011.0	9.2	1873254.0	2010-2020
3812	Pizza Delivery/Home Sweet Pineapple	1999.0	9.2	1441.0	1990-2000
6558	Selena Live: The Last Concert	1995.0	9.2	175.0	1990-2000
2277	Carly Simon Live from Martha's Vineyard	1987.0	9.1	17.0	1980-1990